Hyperparameter Optimization for Machine Learning

• Hyperparameter tunning and why it matters
• Cross-validation and nested cross-validation
• Hyperparameter tunning with Grid and Random search
• Bayesian Optimisation
• Tree-Structured Parzen Estimators, Population Based Training and SMAC
• Hyperparameter tunning tools, i.e., Hyperopt, Optuna, Scikit-optimize, Keras Turner and others

Who this course is for:

• Students who want to know more about hyperparameter optimization algorithms
• Students who want to understand advanced techniques for hyperparameter optimization
• Students who want to learn to use multiple open source libraries for hyperparameter tuning
• Students interested in building better performing machine learning models
• Students interested in participating in data science competitions
• Students seeking to expand their breadth of knowledge on machine learning